def build_stamp(self, page_num=0, layout="", view=None):
if True:
coast_empty = mv.mcoast(
map_coastline="off", map_grid="off", map_label="off"
)
empty_view = mv.geoview(
page_frame="off",
subpage_frame="off",
coastlines=coast_empty,
subpage_x_position=40,
subpage_x_length=10,
subpage_y_length=10,
)
title_page = mv.plot_page(
top=0, bottom=5, left=30, right=70, view=empty_view
)
r, c = self._grid_row_col(page_num=page_num, layout=layout)
pages = mv.mvl_regular_layout(view, c, r, 1, 1, [5, 100, 15, 100])
pages.append(title_page)
return mv.plot_superpage(pages=pages)
def test_met_plot():
contour = mv.mcont({
'CONTOUR_LINE_COLOUR': 'PURPLE',
'CONTOUR_LINE_THICKNESS': 3,
'CONTOUR_HIGHLIGHT': False
})
coast = mv.mcoast({'MAP_COASTLINE_LAND_SHADE': True})
bindings.met_plot(TEST_FIELDSET, contour, coast)
def plot_obs_freq(predictor_matrix, code):
coastline = mv.mcoast(map_coastline_thickness=2,
map_boundaries="on",
map_coastline_colour="chestnut")
symbol = mv.msymb(
legend="on",
symbol_type="marker",
symbol_table_mode="on",
symbol_outline="on",
symbol_min_table=[1, 2, 5, 10, 15, 20, 25, 30],
symbol_max_table=[2, 5, 10, 15, 20, 25, 30, 100000],
symbol_colour_table=[
"RGB(0.7020,0.7020,0.7020)",
"RGB(0.4039,0.4039,0.4039)",
"blue",
"RGB(0.4980,1.0000,0.0000)",
"RGB(1.0000,0.8549,0.0000)",
"orange",
"red",
"magenta",
],
symbol_marker_table=15,
symbol_height_table=0.3,
)
legend = mv.mlegend(
legend_text_font="arial",
legend_text_font_size=0.35,
legend_entry_plot_direction="row",
legend_box_blanking="on",
legend_entry_text_width=50,
)
title = mv.mtext(
text_line_count=4,
text_line_1=
"OBS Frequency", # To sostitute with "FE" values when relevant.
text_line_2=f"WT Code = {code}",
text_line_4=" ",
text_font="arial",
text_font_size=0.4,
)
df = predictor_matrix[["LonOBS", "LatOBS", "OBS"]]
grouped_df = df.groupby(["LatOBS", "LonOBS"], as_index=False).count()
geo = mv.create_geo(len(grouped_df), "xyv")
geo = mv.set_latitudes(geo, grouped_df["LatOBS"].to_numpy(dtype=np.float))
geo = mv.set_longitudes(geo, grouped_df["LonOBS"].to_numpy(dtype=np.float))
geo = mv.set_values(geo, grouped_df["OBS"].to_numpy(dtype=np.float))
with NamedTemporaryFile(delete=False, suffix=".pdf") as pdf:
pdf_obj = mv.pdf_output(output_name=pdf.name.replace(".pdf", ""))
mv.setoutput(pdf_obj)
mv.plot(coastline, symbol, legend, title, geo)
return pdf.name
def plot_std(predictor_matrix, code):
coastline = mv.mcoast(map_coastline_thickness=2,
map_boundaries="on",
map_coastline_colour="chestnut")
symbol = mv.msymb(
legend="on",
symbol_type="marker",
symbol_table_mode="on",
symbol_outline="on",
symbol_min_table=[0, 0.0001, 0.5, 1, 2, 5],
symbol_max_table=[0.0001, 0.5, 1, 2, 5, 1000],
symbol_colour_table=[
"RGB(0.7020,0.7020,0.7020)",
"RGB(0.2973,0.2973,0.9498)",
"RGB(0.1521,0.6558,0.5970)",
"RGB(1.0000,0.6902,0.0000)",
"red",
"RGB(1.0000,0.0000,1.0000)",
],
symbol_marker_table=15,
symbol_height_table=0.3,
)
legend = mv.mlegend(
legend_text_font="arial",
legend_text_font_size=0.35,
legend_entry_plot_direction="row",
legend_box_blanking="on",
legend_entry_text_width=50,
)
error = "FER" if "FER" in predictor_matrix.columns else "FE"
title = mv.mtext(
text_line_count=4,
text_line_1=f"{error} Standard Deviation",
text_line_2=f"WT Code = {code}",
text_line_4=" ",
text_font="arial",
text_font_size=0.4,
)
df = predictor_matrix[["LonOBS", "LatOBS", error]]
grouped_df = df.groupby(["LatOBS", "LonOBS"])[error].mean().reset_index()
geo = mv.create_geo(len(grouped_df), "xyv")
geo = mv.set_latitudes(geo, grouped_df["LatOBS"].to_numpy(dtype=np.float))
geo = mv.set_longitudes(geo, grouped_df["LonOBS"].to_numpy(dtype=np.float))
geo = mv.set_values(geo, grouped_df[error].to_numpy(dtype=np.float))
with NamedTemporaryFile(delete=False, suffix=".pdf") as pdf:
pdf_obj = mv.pdf_output(output_name=pdf.name.replace(".pdf", ""))
mv.setoutput(pdf_obj)
mv.plot(coastline, symbol, legend, title, geo)
return pdf.name
def plot_avg(predictor_matrix, code):
coastline = mv.mcoast(map_coastline_thickness=2,
map_boundaries="on",
map_coastline_colour="chestnut")
symbol = mv.msymb(
legend="on",
symbol_type="marker",
symbol_table_mode="on",
symbol_outline="on",
symbol_min_table=[-1, -0.25, 0.25, 2],
symbol_max_table=[-0.025, 0.25, 2, 1000],
symbol_colour_table=[
"RGB(0.0000,0.5490,0.1882)",
"black",
"RGB(1.0000,0.6902,0.0000)",
"red",
],
symbol_marker_table=15,
symbol_height_table=0.3,
)
legend = mv.mlegend(
legend_text_font="arial",
legend_text_font_size=0.35,
legend_entry_plot_direction="row",
legend_box_blanking="on",
legend_entry_text_width=50,
)
error = "FER" if "FER" in predictor_matrix.columns else "FE"
title = mv.mtext(
text_line_count=4,
text_line_1=f"{error} Mean",
text_line_2=f"WT Code = {code}",
text_line_4=" ",
text_font="arial",
text_font_size=0.4,
)
df = predictor_matrix[["LonOBS", "LatOBS", error]]
grouped_df = df.groupby(["LatOBS", "LonOBS"])[error].mean().reset_index()
geo = mv.create_geo(len(grouped_df), "xyv")
geo = mv.set_latitudes(geo, grouped_df["LatOBS"].to_numpy(dtype=np.float))
geo = mv.set_longitudes(geo, grouped_df["LonOBS"].to_numpy(dtype=np.float))
geo = mv.set_values(geo, grouped_df[error].to_numpy(dtype=np.float))
return plot_geo(geo, coastline, symbol, legend, title)
def test_definitions():
mcont_def = mv.mcont({'legend': True})
msymb_def = mv.msymb({'symbol_type': 'marker'})
mcoast_def = mv.mcoast({'map_coastline_land_shade': True})
mobs_def = mv.mobs({'obs_temperature': False})
mtext_def = mv.mtext({'text_font_size': '0.80'})
geoview_def = mv.geoview({'map_projection': 'polar_stereographic'})
ps_output_def = mv.ps_output({'output_name': 'test'})
assert mcont_def['LEGEND'] == 'ON'
assert msymb_def['SYMBOL_TYPE'] == 'MARKER'
assert mcoast_def['MAP_COASTLINE_LAND_SHADE'] == 'ON'
assert mobs_def['OBS_TEMPERATURE'] == 'OFF'
assert mtext_def['TEXT_FONT_SIZE'] == 0.8
assert geoview_def['MAP_PROJECTION'] == 'POLAR_STEREOGRAPHIC'
assert ps_output_def['OUTPUT_NAME'] == 'test'
to alter its plotting style
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
3. Python analyst reads BUFR data and filters a single parameter from it
and plots it with a colour scale
--------------------------------------------------------------------------------
"""
import metview as mv
# define a view over the area of interest and set land shading on
land_shade = mv.mcoast(
map_coastline_land_shade=True,
map_coastline_land_shade_colour="RGB(0.98,0.95,0.82)",
map_coastline_sea_shade=False,
map_coastline_sea_shade_colour="RGB(0.85,0.93,1)",
)
area_view = mv.geoview(
map_area_definition="corners",
area=[45.83, -13.87, 62.03, 8.92],
coastlines=land_shade,
)
# Simplest plot:
# NOTE that when plotting a 'raw' BUFR file, Magics will plot synop symbols as shown in
# https://software.ecmwf.int/wiki/display/METV/Data+Part+1 "Plotting BUFR Data"
obs = mv.read("../tests/obs_3day.bufr")